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Energy Efficiency Optimization using AutomationML modeling and an EnPI methodology

: Thiele, Gregor; Khorsandi, Niloufar; Krüger, Jörg


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019. Proceedings : Zaragoza, Spain, 10 - 13 September 2019
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-0303-7
ISBN: 978-1-7281-0302-0
ISBN: 978-1-7281-0304-4
International Conference on Emerging Technologies and Factory Automation (ETFA) <24, 2019, Zaragoza>
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
03ET1313B; EnEffReg
Conference Paper
Fraunhofer IPK ()
energy efficiency; performance optimization; AutomationML; plant modeling; industrial cooling system

Industrial facilities are complex and heterogeneous systems in permanent technological change. The ambitions towards smart factories heighten the requirements for the flexible interconnection of various devices. These industrial entities are controlled, observed and optimized by many services. The tuning of process parameters of several linked components in order to boost the overall energy efficiency is one example of such services. AutomationML (AML) provides a hierarchical description language for industrial systems considering both structure and properties. An extension of the established standard allows for intuitive modeling of energy optimization problems. An approved energy performance indicator (EnPI) methodology was integrated in the libraries of AML in order to simplify and shorten the modeling procedure for the optimization task. The procedure is demonstrated using the example of an industrial cooling system.